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1. Injecting Language Workbench Technology into Mainstream Languages

   Ballantyne, Michael and (April 2023, Open access series in informatics)

   Lammel, Ralf and (Ed.)

   Eelco Visser envisioned a future where DSLs become a commonplace abstraction in software development. He took strides towards implementing this vision with the Spoofax language workbench. However, his vision is far from the mainstream of programming today. How will the many mainstream programmers encounter and adopt language workbench technology? We propose that the macro systems found in emerging industrial languages open a path towards delivering language workbenches as easy-to-adopt libraries. To develop the idea, we sketch an implementation of a language workbench as a macro-library atop Racket and identify the key features of the macro system needed to enable this evolution path. DOI: 10.4230/OASIcs.EVCS.2023.3 (but your system choked on it) 

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2. Cross-modal Map Learning for Vision and Language Navigation

   https://doi.org/10.1109/CVPR52688.2022.01502  

   Georgakis, Georgios; Schmeckpeper, Karl; Wanchoo, Karan; Dan, Soham; Miltsakaki, Eleni; Roth, Dan; Daniilidis, Kostas (June 2022, IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR))

   We consider the problem of Vision-and-Language Navigation (VLN). The majority of current methods for VLN are trained end-to-end using either unstructured memory such as LSTM, or using cross-modal attention over the egocentric observations of the agent. In contrast to other works, our key insight is that the association between language and vision is stronger when it occurs in explicit spatial representations. In this work, we propose a cross-modal map learning model for vision-and-language navigation that first learns to predict the top-down semantics on an egocentric map for both observed and unobserved regions, and then predicts a path towards the goal as a set of way-points. In both cases, the prediction is informed by the language through cross-modal attention mechanisms. We experimentally test the basic hypothesis that language-driven navigation can be solved given a map, and then show competitive results on the full VLN-CE benchmark. 

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3. NDH-Full: Learning and Evaluating Navigational Agents on Full-Length Dialogue

   Kim, Hyounghun; Li, Jialu; Bansal, Mohit (November 2021, Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing)

   null (Ed.)

   Communication between human and mobile agents is getting increasingly important as such agents are widely deployed in our daily lives. Vision-and-Dialogue Navigation is one of the tasks that evaluate the agent’s ability to interact with humans for assistance and navigate based on natural language responses. In this paper, we explore the Navigation from Dialogue History (NDH) task, which is based on the Cooperative Vision-and-Dialogue Navigation (CVDN) dataset, and present a state-of-the-art model which is built upon Vision-Language transformers. However, despite achieving competitive performance, we find that the agent in the NDH task is not evaluated appropriately by the primary metric – Goal Progress. By analyzing the performance mismatch between Goal Progress and other metrics (e.g., normalized Dynamic Time Warping) from our state-of-the-art model, we show that NDH’s sub-path based task setup (i.e., navigating partial trajectory based on its correspondent subset of the full dialogue) does not provide the agent with enough supervision signal towards the goal region. Therefore, we propose a new task setup called NDH-Full which takes the full dialogue and the whole navigation path as one instance. We present a strong baseline model and show initial results on this new task. We further describe several approaches that we try, in order to improve the model performance (based on curriculum learning, pre-training, and data-augmentation), suggesting potential useful training methods on this new NDH-Full task. 

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4. Language and Speech Technology for Central Kurdish Varieties

   Ahmadi, Sina; Jaff, Daban; Ibn_Alam, Md_Mahfuz; Anastasopoulos, Antonios (May 2024, ELRA and ICCL)

   Kurdish, an Indo-European language spoken by over 30 million speakers, is considered a dialect continuum and known for its diversity in language varieties. Previous studies addressing language and speech technology for Kurdish handle it in a monolithic way as a macro-language, resulting in disparities for dialects and varieties for which there are few resources and tools available. In this paper, we take a step towards developing resources for language and speech technology for varieties of Central Kurdish, creating a corpus by transcribing movies and TV series as an alternative to fieldwork. Additionally, we report the performance of machine translation, automatic speech recognition, and language identification as downstream tasks evaluated on Central Kurdish subdialects. Data and models are publicly available under an open license at https://github.com/sinaahmadi/CORDI. 

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5. Reproducible Software Environment: a tool enabling computational reproducibility in geospace sciences and facilitating collaboration

   https://doi.org/10.1051/swsc/2020011  

   Bhatt, Asti; Valentic, Todd; Reimer, Ashton; Lamarche, Leslie; Reyes, Pablo; Cosgrove, Russell (January 2020, Journal of Space Weather and Space Climate)

   The Reproducible Software Environment (Resen) is an open-source software tool enabling computationally reproducible scientific results in the geospace science community. Resen was developed as part of a larger project called the Integrated Geoscience Observatory (InGeO), which aims to help geospace researchers bring together diverse datasets from disparate instruments and data repositories, with software tools contributed by instrument providers and community members. The main goals of InGeO are to remove barriers in accessing, processing, and visualizing geospatially resolved data from multiple sources using methodologies and tools that are reproducible. The architecture of Resen combines two mainstream open source software tools, Docker and JupyterHub, to produce a software environment that not only facilitates computationally reproducible research results, but also facilitates effective collaboration among researchers. In this technical paper, we discuss some challenges for performing reproducible science and a potential solution via Resen, which is demonstrated using a case study of a geospace event. Finally we discuss how the usage of mainstream, open-source technologies seems to provide a sustainable path towards enabling reproducible science compared to proprietary and closed-source software. 

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